Optimum Job Support — Export & Accelerate on Target Hardware

We provide hands-on, real-time Optimum proxy job support on your actual project tickets. We help you export models to ONNX/ORT or compile them for Trainium/Inferentia with Optimum-Neuron, and validate parity and speed. This page is part of our Hugging Face proxy job support ecosystem: our experts help with architecture and implementation, environment and dependency setup, debugging, code review, performance/GPU-memory tuning, and production issues — during your working hours, same-day. "Proxy" means expert technical support and mentoring on your real deliverables, not replacing you or performing your job duties.

Typical Optimum production issues we resolve include CUDA out-of-memory and GPU-memory pressure, dependency and version conflicts (Transformers/PyTorch/CUDA), dtype and precision mismatches, slow throughput or latency, checkpoint and safetensors loading errors, and integration failures with upstream and downstream systems. We help you find the root cause from stack traces, logs, and profiling, then ship a stable fix.

Yes. We provide Optimum proxy interview support (also searched as Optimum interview proxy support) — real-time expert help on fundamentals, architecture and design trade-offs, scenario-based problems, and hands-on coding rounds — calibrated to the exact role and company format. Proxy interview support means real-time technical help on the exact areas your interview covers; you attend and complete your own interview.

Yes. Onboarding onto an unfamiliar Optimum setup is one of the most common reasons people reach out. We help you understand the existing model and data pipeline, the training and serving architecture, and the repository structure, get productive fast, deliver your first tasks confidently, and avoid the mistakes that get flagged in reviews and standups.

Contact us on WhatsApp with your stack, the problem, and your timeline. We assign the right expert — usually same-day. Every engagement is fully confidential, and NDAs are available on request.

We look at the exact op and opset. Fixes range from bumping the opset, to swapping an unsupported module for an equivalent, to exporting a sub-graph, to using a newer optimum-onnx version where the op is supported. We then validate numerical parity between the PyTorch and ONNX outputs so you trust the exported model before it ships.

When you run at enough scale on AWS that Inferentia/Trainium’s price-performance beats general GPUs. The catch is ahead-of-time compilation and some model-support constraints. We help you assess whether your model and workload fit, compile it with Optimum-Neuron, and benchmark the real cost/latency versus GPU serving before you commit.